meta-learning — 7 Technical Articles

Jul 6, 2023 · Artificial Intelligence

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

This article presents Tencent TRS's industrial deployment of meta‑learning and cross‑domain recommendation, detailing problem definitions, solution architectures, challenges of industrialization, and practical implementations that achieve personalized modeling and cost‑effective multi‑scene recommendation across various online services.

Industrial AIMAMLcross-domain

0 likes · 18 min read

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

DataFunSummit

Aug 21, 2021 · Artificial Intelligence

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

This article reviews algorithmic solutions for cold‑start recommendation, covering the efficient use of side information, knowledge graphs, cross‑domain transfer, multi‑behavior signals, limited interaction data, explore‑exploit tactics, and additional practical scenarios, while summarizing key methods such as DropoutNet, MetaEmbedding, MWUF, MeLU and MetaHIN.

Recommender Systemscold-startcross-domain

0 likes · 11 min read

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

AntTech

Mar 21, 2021 · Artificial Intelligence

Hubble Intelligent Audience Platform: Three‑Generation Algorithm Evolution for Mobile Marketing

The article describes the Hubble Intelligent Audience Platform’s three‑generation algorithmic evolution—starting from a DSSM‑based model, moving to an asynchronous GNN plus lightweight learning architecture, and finally integrating incremental learning with meta‑weighting—to improve audience expansion for mobile marketing campaigns.

AIMobile Marketingaudience expansion

0 likes · 14 min read

Hubble Intelligent Audience Platform: Three‑Generation Algorithm Evolution for Mobile Marketing

DataFunTalk

Dec 30, 2020 · Artificial Intelligence

Meta-Dialog System: Using Meta-Learning for Fast Adaptation and Robustness in Task-Oriented Conversational AI

This article presents a meta‑learning based end‑to‑end task‑oriented dialogue system that quickly adapts to new scenarios with limited data and improves robustness through a human‑machine collaboration decision module, validated on extended‑bAbI benchmarks and real‑world Alibaba Cloud customer‑service applications.

Few‑Shot LearningMAMLdialogue system

0 likes · 15 min read

Meta-Dialog System: Using Meta-Learning for Fast Adaptation and Robustness in Task-Oriented Conversational AI

DataFunTalk

Nov 22, 2019 · Artificial Intelligence

Machine Reasoning for Multi‑turn Semantic Parsing and Question Answering

This article reviews recent advances in machine reasoning applied to multi‑turn semantic parsing and conversational question answering, describing how grammar, context, and data knowledge are integrated via sequence‑to‑action models and meta‑learning to achieve state‑of‑the‑art results on the CSQA benchmark.

conversational QAmachine reasoningmeta-learning

0 likes · 8 min read

Machine Reasoning for Multi‑turn Semantic Parsing and Question Answering

DataFunTalk

Sep 19, 2019 · Artificial Intelligence

Alibaba Cloud Xiaomai Dialogue System: Architecture, NLU, Dialogue Management, and User Simulator

This article presents Alibaba's Xiaomai intelligent dialogue platform, detailing its general system architecture, three-tier NLU approaches for zero‑, few‑, and many‑shot scenarios, platform‑centric dialogue management with TaskFlow, robustness and continuous learning mechanisms, and a user simulator for large‑scale data generation and dialogue diagnosis.

dialogue systemmeta-learningnatural language understanding

0 likes · 13 min read

Alibaba Cloud Xiaomai Dialogue System: Architecture, NLU, Dialogue Management, and User Simulator

Hulu Beijing

Mar 26, 2019 · Artificial Intelligence

Meta-Learning Explained: Core Concepts, Scenarios, and Few-Shot Learning Benefits

This article introduces meta‑learning (learning to learn), its historical roots, explains why it excels in small‑sample and multi‑task settings, contrasts it with supervised and reinforcement learning, and outlines the theoretical reasons it enables rapid few‑shot adaptation.

Few‑Shot Learningmachine learningmeta-learning

0 likes · 8 min read

Meta-Learning Explained: Core Concepts, Scenarios, and Few-Shot Learning Benefits